This OSF project hosts online supplementary materials for a paper proposing 2-stage maximum likelhood estimation of structural equation models for round-robin data (see Abstract below). The online materials include the data we analyzed (see the paper for the source) and the software syntax used for analysis. All analyses can be conducted in R, and files are named in accordance with the appendices in our publication (see Citation below). Appendices in the paper only include a subset of our reported analyses, all of which are provided on this repository. The published version of our paper is also provided, with full details. The **Related Preprints** folder contains some papers with related simulation evidence, although those projects were still works in progress when this proof-of-concept paper was published. ## Citation Jorgensen, T. D., Bhangale, A. M. & Rosseel, Y. (2024). Two-stage limited-information estimation for structural equation models of round-robin variables. *Stats, 7*(1), 235–268. https://doi.org/10.3390/stats7010015 ## Abstract We propose and demonstrate a new 2-stage maximum likelihood estimator for parameters of the social-relations structural equation model (SR-SEM), using estimated summary statistics ($\widehat\Sigma$) as data, as well as uncertainty about $\widehat\Sigma$ to obtain robust inferential statistics. The SR-SEM is a generalization of traditional SEM for round-robin data, which have a dyadic network structure (i.e., each group member responds about or interacts with each other member). Our two-stage estimator is developed using similar logic as previous two-stage estimators for SEM, developed for applications to multilevel data and multiple imputations of missing data. We demonstrate out estimator on a publicly available data set from a 2018 publication about social mimicry. We employ Markov chain Monte Carlo estimation of summary statistics in Stage 1, implemented using the R package `rstan`. In Stage 2, the posterior mean estimates of summary statistics are used as input data to estimate SEM parameters with the R package `lavaan`. The posterior covariance matrix of estimated summary statistics is also calculated, so that `lavaan` can use it to calculate robust standard errors and test statistics. Results are compared to full information maximum likelihood (FIML) estimation of SR-SEM parameters using the R package `srm`. We discuss how differences between estimators highlight the need for future research to establish best practices under realistic conditions (e.g., how to specify empirical-Bayes priors in Stage 1), as well as extensions that would make two-stage estimation particularly advantageous over single-stage FIML.